Agentic AI-Enabled Solar-Powered High-Altitude Platforms for Sustainable SAGINs

Intelligent Multi-Objective Optimization for UAV Mission Decision Support

Solar-powered Unmanned Aerial Vehicles (UAVs) and High-Altitude Pseudo-Satellites (HAPS) offer significant potential for persistent intelligence, surveillance and reconnaissance operations, but their endurance remains constrained by variable solar irradiance, atmospheric turbulence, battery limitations and payload power demand. This review examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in improving UAV decision-support optimization through predictive solar forecasting, reinforcement-learning-based flight-path optimization, adaptive Maximum Power Point Tracking (MPPT), battery state estimation and intelligent payload load balancing. The paper synthesizes current approaches using Long Short-Term Memory networks, gradient-boosting models, convolutional neural networks, graph neural networks and reinforcement learning architectures for autonomous energy-aware flight. Simulation-based analyses suggest that AI-assisted control can improve energy utilization by 15-25% and extend mission persistence by 30-40%, particularly under variable irradiance and wind conditions. However, practical deployment requires robust validation, certifiable AI architectures, adversarial resilience and reliable edge-computing implementation. The strategic deployment of AI-enabled autonomous UAVs directly supports Saudi Arabia's Vision 2030 objectives for indigenous defence technology development, artificial intelligence research, and advanced aerospace systems. By establishing domestic expertise in AI-driven decision-support optimization, the Kingdom advances its defence industrial sovereignty while creating high-value technical employment in autonomous systems engineering and aerospace artificial intelligence.

Sustainable Air-Ground Integrated Coverage Networks: ISCC Architecture, Technologies, and Testbed

A multi-dimensional sustainability metric system is introduced, which covers operational efficiency, task-oriented performance, and full lifecycle carbon emissions, to quantify energy and carbon footprints and demonstrate the feasibility of sustainable AGICNs for future green networks.

Journal of Intelligent Decision Making and Information Science

Joint Beamforming, Energy Management, and Trajectory Optimization for Figure-Eight Loitering in Solar-Powered HAPS-Enabled ISAC Systems

Solar-powered high-altitude platform stations (HAPSs) provide a promising platform for integrated sensing and communication (ISAC) owing to their wide-area coverage and long-endurance operation. This paper proposes a solar-powered HAPS-enabled ISAC framework for sustainable day-night operation, where a figure-eight loitering architecture is adopted to provide persistent ISAC services over geographically separated regions while harvesting solar energy. A unified communication-sensing-energy model is developed by jointly characterizing solar energy harvesting, battery dynamics, propulsion power consumption, communication transmission, and synthetic aperture radar (SAR) imaging. Based on this model, coupled optimization problems are formulated for daytime operation (DTO) and nighttime operation (NTO), where the battery state bridges the two operational phases through a long-term energy budget. The proposed framework jointly optimizes communication, sensing, mobility, and energy management to maximize daytime communication performance while minimizing nighttime propulsion energy consumption. Efficient iterative algorithms are developed to solve the resulting non-convex optimization problems. Simulation results verify the effectiveness of the proposed communication-sensing-energy co-design and demonstrate that the proposed framework effectively supports sustainable day-night ISAC operation.

High-Altitude Platforms Beyond Connectivity: A Survey of Integrated Sensing, Storage, Communication, Computing, and Intelligence

High-altitude platforms (HAPs) are emerging as persistent middle-layer infrastructures for space-air-ground integrated networks (SAGINs), offering a favorable compromise among coverage, latency, endurance, and deployment flexibility. Their role, however, is evolving beyond communication relaying toward the joint provision of sensing, storage, communication, computing, and intelligence (S^2C^2I). This survey presents a unified HAP-centric perspective on S^2C^2I integration. We first review HAP fundamentals, platform categories, and their principal roles in SAGINs, including wide-area access, relaying, backhaul, edge service, low-altitude aerial coordination, and cross-layer orchestration. We then develop an integrated architecture spanning multi-plane connectivity, payload functional splits, and a cloud-edge-HAP space continuum with hierarchical data, control, computing, and storage loops. The enabling technologies are systematically examined, covering heterogeneous RF, millimeter-wave, terahertz, free-space optical, and hybrid links; sensing payloads and integrated sensing and communication; onboard computing; storage and caching; and AI-based orchestration. We further synthesize standardization progress, open software and datasets, testbeds, field evidence, and a four-level evaluation methodology ranging from component validation to mission-level effectiveness. An emergency-response case study demonstrates that joint S^2C^2I orchestration substantially improves conjunctive service availability while reducing feeder-link traffic. Finally, we identify research opportunities in agentic AI, trustworthy autonomy, goal-oriented semantic operation and digital twins, and sustainable, certifiable, and open HAP-native systems. The resulting synthesis provides a coherent roadmap from platform design to network-wide deployment.

Artificial Intelligence in UAV Energy Management: Predictive Algorithms for Solar-Powered Stratospheric Flight Optimization and Autonomous Mission Persistence.

Solar-powered Unmanned Aerial Vehicles (UAVs) and High-Altitude Pseudo-Satellites (HAPS) offer significant potential for persistent intelligence, surveillance and reconnaissance operations, but their endurance remains constrained by variable solar irradiance, atmospheric turbulence, battery limitations and payload power demand. This review examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in improving UAV energy management through predictive solar forecasting, reinforcement-learning-based flight-path optimization, adaptive Maximum Power Point Tracking (MPPT), battery state estimation and intelligent payload load balancing. The paper synthesizes current approaches using Long Short-Term Memory networks, gradient-boosting models, convolutional neural networks, graph neural networks and reinforcement learning architectures for autonomous energy-aware flight. Simulation-based analyses suggest that AI-assisted control can improve energy utilization by 15-25% and extend mission persistence by 30-40%, particularly under variable irradiance and wind conditions. However, practical deployment requires robust validation, certifiable AI architectures, adversarial resilience and reliable edge-computing implementation. The strategic deployment of AI-enabled autonomous UAVs directly supports Saudi Arabia's Vision 2030 objectives for indigenous defence technology development, artificial intelligence research, and advanced aerospace systems. By establishing domestic expertise in AI-driven energy management, the Kingdom advances its defence industrial sovereignty while creating high-value technical employment in autonomous systems engineering and aerospace artificial intelligence.